Additional file 3: Supplement Table 3. Parameter values of the common differentially expressed genes

The biomarker discovery outsourcing service market is estimated to be valued at US$ 13.3 billion in 2024. The biomarker discovery outsourcing service market is predicted to rise at a CAGR of 12.2% from 2024 to 2034. The global biomarker discovery outsourcing service market is anticipated to reach US$ 41.4 billion by 2034.

Attributes	Key Insights
Estimated Market Size in 2024	US$ 13.3 billion
Projected Market Value in 2034	US$ 41.4 billion
Value-based CAGR from 2024 to 2034	12.2%

2019 to 2023 Historical Analysis vs. 2024 to 2034 Market Forecast Projections

Historical CAGR from 2019 to 2023	15.6%
Forecast CAGR from 2024 to 2034	12.2%

Country-wise Analysis

Countries	Forecast CAGRs from 2024 to 2034
The United States	12.4%
The United Kingdom	13.3%
China	12.9%
Japan	13.9%
South Korea	14.0%

Category wise Insights

Category	CAGR from 2024 to 2034
Surrogate End-point	12.0%
Genomic Biomarker Services	11.8%

Report Scope

Attributes	Details
Estimated Market Size in 2024	US$ 31.1 billion
Projected Market Valuation in 2034	US$ 41.4 billion
Value-based CAGR 2024 to 2034	12.2%
Forecast Period	2024 to 2034
Historical Data Available for	2019 to 2023
Market Analysis	Value in US$ billion
Key Regions Covered	North America Latin America Western Europe Eastern Europe South Asia and Pacific East Asia The Middle East and Africa
Key Market Segments Covered	Type Service Therapeutic Area End User Region
Key Countries Profiled	The United States Canada Brazil Mexico Germany The United Kingdom France Spain Italy Russia Poland Czech Republic Romania India Bangladesh Australia New Zealand China Japan South Korea GCC countries South Africa Israel
Key Companies Profiled	Bio-Rad Laboratories, Inc. Parexel International (MA) Corporation Svar Life Science Sino Biological Inc. ICON plc. Integrated DNA Technologies Inc. Almac Group Limited REPROCELL Inc. Frontage Labs Biomcare ApS Crown Bioscience

The global Biological Data Analysis Services market is experiencing robust growth, driven by the increasing volume of biological data generated from high-throughput technologies like next-generation sequencing and advanced imaging techniques. The market's expansion is further fueled by the rising demand for personalized medicine, the growing adoption of bioinformatics tools and cloud-based solutions, and increasing investments in research and development across various sectors including pharmaceutical, biotechnology, and academic research. Key application areas such as biomarker identification, biological modeling, and image analysis are witnessing significant traction, contributing substantially to the market's overall growth. The diverse range of services offered, encompassing statistical data analysis and programming, data visualization, and structural biology, caters to the varied needs of researchers and organizations. Segments like biomarker identification and biological modeling are anticipated to exhibit faster growth compared to others owing to their crucial role in drug discovery and development. North America and Europe currently dominate the market, owing to established research infrastructure and higher healthcare expenditure, but the Asia-Pacific region is projected to show rapid growth due to increasing investments in life sciences research and development, and the expanding biotechnology sector. Competitive landscape analysis reveals a mix of large multinational corporations and specialized service providers. While established players like Eurofins Scientific leverage their extensive network and resources, smaller specialized companies are focusing on niche areas such as specific bioinformatics solutions or particular biological data types, offering innovative and tailored services. This competition is driving innovation and improvement in the quality and accessibility of biological data analysis services. Restraints to market growth include the high cost of advanced analytical tools and the need for specialized expertise to handle complex datasets. However, ongoing technological advancements and the development of user-friendly software are mitigating these challenges. Over the forecast period (2025-2033), continued innovation, particularly in AI and machine learning driven analysis, is expected to further fuel market expansion, leading to improved efficiency and affordability of biological data analysis.

The global genomic biomarkers market is experiencing robust growth, driven by the increasing prevalence of chronic diseases, advancements in genomic technologies, and rising demand for personalized medicine. The market, valued at approximately $25 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated market size of $80 billion by 2033. This significant expansion is fueled by several key factors. The development of high-throughput screening technologies and sophisticated bioinformatics tools allows for faster and more cost-effective analysis of genomic data, accelerating biomarker discovery and clinical translation. Furthermore, the increasing adoption of personalized medicine approaches, which tailor treatments based on an individual's genetic profile, significantly boosts demand for genomic biomarkers across various therapeutic areas. The segments driving this growth are oncology and cardiovascular diseases, reflecting the high prevalence and unmet medical needs in these areas. Diagnostic and research laboratories constitute a significant portion of the market, indicating a strong reliance on genomic biomarkers for both research and clinical diagnostics. However, challenges such as regulatory hurdles in obtaining approvals for new biomarkers and the high cost associated with genomic testing remain as potential restraints. Despite these restraints, the market's future outlook remains positive. Continued technological advancements, such as next-generation sequencing and liquid biopsy techniques, will further enhance the accuracy, speed, and cost-effectiveness of genomic biomarker testing. Furthermore, increasing investments in research and development by both pharmaceutical companies and government agencies are fostering the development of innovative genomic biomarkers and diagnostic tools. The rising awareness among healthcare professionals and the general public regarding the benefits of genomic testing also contribute positively to market growth. The geographic distribution reveals strong growth across North America and Europe, but significant opportunities also exist in rapidly developing economies in Asia Pacific, particularly in China and India, as healthcare infrastructure improves and access to advanced diagnostic tools increases.

Supplemental data of the manuscript named 'Ferroptosis-Driven Risk Model for Prognostic Assessment, Immune Profiling and Drug sensitivity in Lung Adenocarcinoma'

Additional file 1: Supplement Table 1. The differentially expressed genes of GSE25097

Additional file 9. Gene ontology and biological pathway of co-expressed genes with PKMYT1.

This work presents a new consensus clustering method for gene expression microarray data based on a genetic algorithm. Using two datasets - DA and DB - as input, the genetic algorithm examines putative partitions for the samples in DA, selecting biomarkers that support such partitions. The biomarkers are then used to build a classifier which is used in DB to determine its samples classes. The genetic algorithm is guided by an objective function that takes into account the accuracy of classification in both datasets, the number of biomarkers that support the partition, and the distribution of the samples across the classes for each dataset. To illustrate the method, two whole-genome breast cancer instances from dfferent sources were used. In this application, the results indicate that the method could be used to find unknown subtypes of diseases supported by biomarkers presenting similar gene expression profiles across platforms. Moreover, even though this initial study was restricted to two datasets and two classes, the method can be easily extended to consider both more datasets and classes. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

Additional file 10. Gene ontology and biological pathway of co-expressed genes with EPYC.

Additional file 8. Gene ontology and biological pathway of co-expressed genes with CACNG4.

Additional file 11. Gene ontology and biological pathway of co-expressed genes with CHRNA6.

ObjectiveParkinson’s disease (PD) is the most common neurodegenerative movement disorder and immune-mediated mechanism is considered to be crucial to pathogenesis. Here, we investigated the role of humoral immune regulatory molecules in the pathogenesis of PD.MethodsFirstly, we performed a series of bioinformatic analyses utilizing the expression profile of the peripheral blood mononuclear cell (PBMC) obtained from the GEO database (GSE100054, GSE49126, and GSE22491) to identify differentially expressed genes related to humoral immune regulatory mechanisms between PD and healthy controls. Subsequently, we verified the results using quantitative polymerase chain reaction (Q-PCR) and enzyme-linked immunosorbent assay (ELISA) in clinical blood specimen. Lastly, receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic effects of verified molecules.ResultsWe obtained 13 genes that were mainly associated with immune-related biological processes in PD using bioinformatic analysis. Then, we selected PPBP, PROS1, and LCN2 for further exploration. Fascinatingly, our experimental results don’t always coincide with the expression profile. PROS1 and LCN2 plasma levels were significantly higher in PD patients compared to controls (p < 0.01 and p < 0.0001). However, the PPBP plasma level and expression in the PBMC of PD patients was significantly decreased compared to controls (p < 0.01 and p < 0.01). We found that PPBP, PROS1, and LCN2 had an area under the curve (AUC) of 0.663 (95%CI: 0.551–0.776), 0.674 (95%CI: 0.569–0.780), and 0.885 (95%CI: 0.814–0.955). Furthermore, in the biological process analysis of gene ontology (GO), the three molecules were all involved in humoral immune response (GO:0006959).ConclusionsIn general, PPBP, PROS1, and LCN2 were identified and validated to be related to PD and PPBP, LCN2 may potentially be biomarkers or therapeutic targets for PD. Our findings also provide some new insights on the humoral immune modulation mechanisms in PD.

ObjectiveParkinson’s disease (PD) is the most common neurodegenerative movement disorder and immune-mediated mechanism is considered to be crucial to pathogenesis. Here, we investigated the role of humoral immune regulatory molecules in the pathogenesis of PD.MethodsFirstly, we performed a series of bioinformatic analyses utilizing the expression profile of the peripheral blood mononuclear cell (PBMC) obtained from the GEO database (GSE100054, GSE49126, and GSE22491) to identify differentially expressed genes related to humoral immune regulatory mechanisms between PD and healthy controls. Subsequently, we verified the results using quantitative polymerase chain reaction (Q-PCR) and enzyme-linked immunosorbent assay (ELISA) in clinical blood specimen. Lastly, receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic effects of verified molecules.ResultsWe obtained 13 genes that were mainly associated with immune-related biological processes in PD using bioinformatic analysis. Then, we selected PPBP, PROS1, and LCN2 for further exploration. Fascinatingly, our experimental results don’t always coincide with the expression profile. PROS1 and LCN2 plasma levels were significantly higher in PD patients compared to controls (p < 0.01 and p < 0.0001). However, the PPBP plasma level and expression in the PBMC of PD patients was significantly decreased compared to controls (p < 0.01 and p < 0.01). We found that PPBP, PROS1, and LCN2 had an area under the curve (AUC) of 0.663 (95%CI: 0.551–0.776), 0.674 (95%CI: 0.569–0.780), and 0.885 (95%CI: 0.814–0.955). Furthermore, in the biological process analysis of gene ontology (GO), the three molecules were all involved in humoral immune response (GO:0006959).ConclusionsIn general, PPBP, PROS1, and LCN2 were identified and validated to be related to PD and PPBP, LCN2 may potentially be biomarkers or therapeutic targets for PD. Our findings also provide some new insights on the humoral immune modulation mechanisms in PD.

Additional file 1.

BackgroundAlzheimer’s disease (AD) is the most prevalent form of dementia, and is becoming one of the most burdening and lethal diseases. More useful biomarkers for diagnosing AD and reflecting the disease progression are in need and of significance.MethodsThe integrated bioinformatic analysis combined with machine-learning strategies was applied for exploring crucial functional pathways and identifying diagnostic biomarkers of AD. Four datasets (GSE5281, GSE131617, GSE48350, and GSE84422) with samples of AD frontal cortex are integrated as experimental datasets, and another two datasets (GSE33000 and GSE44772) with samples of AD frontal cortex were used to perform validation analyses. Functional Correlation enrichment analyses were conducted based on Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Reactome database to reveal AD-associated biological functions and key pathways. Four models were employed to screen the potential diagnostic biomarkers, including one bioinformatic analysis of Weighted gene co-expression network analysis (WGCNA)and three machine-learning algorithms: Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) analysis. The correlation analysis was performed to explore the correlation between the identified biomarkers with CDR scores and Braak staging.ResultsThe pathways of the immune response and oxidative stress were identified as playing a crucial role during AD. Thioredoxin interacting protein (TXNIP), early growth response 1 (EGR1), and insulin-like growth factor binding protein 5 (IGFBP5) were screened as diagnostic markers of AD. The diagnostic efficacy of TXNIP, EGR1, and IGFBP5 was validated with corresponding AUCs of 0.857, 0.888, and 0.856 in dataset GSE33000, 0.867, 0.909, and 0.841 in dataset GSE44770. And the AUCs of the combination of these three biomarkers as a diagnostic tool for AD were 0.954 and 0.938 in the two verification datasets.ConclusionThe pathways of immune response and oxidative stress can play a crucial role in the pathogenesis of AD. TXNIP, EGR1, and IGFBP5 are useful biomarkers for diagnosing AD and their mRNA level may reflect the development of the disease by correlation with the CDR scores and Breaking staging.

Additional file 1.

MicroRNAs (miRNAs) are small non-coding RNAs with the potential as biomarkers for disease diagnosis, prognosis and therapy. In the era of big data and biomedical informatics, computer-aided biomarker discovery has become the current frontier. However, most of the computational models are highly dependent on specific prior knowledge and training-testing procedures, very few are mechanism-guided or evidence-based. To the best of our knowledge, untill now no general rules have been uncovered and applied to miRNA biomarker screening. In this study, we manually collected literature-reported cancer miRNA biomarkers and analyzed their regulatory patterns, including the regulatory modes, biological functions and evolutionary characteristics of their targets in the human miRNA-mRNA network. Two evidences were statistically detected and used to distinguish biomarker miRNAs from others. Based on these observations, we developed a novel bioinformatics model and software tool for miRNA biomarker discovery (http://sysbio.suda.edu.cn/MiRNA-BD/). In contrast to routine methods that focus on miRNA synergic functions, our method searches for vulnerable sites in the miRNA-mRNA network and considers the independent regulatory power of miRNAs, i.e., single-line regulations between miRNAs and mRNAs. The performance comparison demonstrates the generality and precision of our model, which identifies miRNA biomarkers for cancers as well as other complex diseases without training or specific prior knowledge.

Background: Hepatocellular carcinoma (HCC) accounts for the majority of liver cancer, with the incidence and mortality rates increasing every year. Despite the improvement of clinical management, substantial challenges remain due to its high recurrence rates and short survival period. This study aimed to identify potential diagnostic and prognostic biomarkers in HCC through bioinformatic analysis.Methods: Datasets from GEO and TCGA databases were used for the bioinformatic analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out by WebGestalt website and clusterProfiler package of R. The STRING database and Cytoscape software were used to establish the protein-protein interaction (PPI) network. The GEPIA website was used to perform expression analyses of the genes. The miRDB, miRWalk, and TargetScan were employed to predict miRNAs and the expression levels of the predicted miRNAs were explored via OncomiR database. LncRNAs were predicted in the StarBase and LncBase while circRNA prediction was performed by the circBank. ROC curve analysis and Kaplan-Meier (KM) survival analysis were performed to evaluate the diagnostic and prognostic value of the gene expression, respectively.Results: A total of 327 upregulated and 422 downregulated overlapping DEGs were identified between HCC tissues and noncancerous liver tissues. The PPI network was constructed with 89 nodes and 178 edges and eight hub genes were selected to predict upstream miRNAs and ceRNAs. A lncRNA/circRNA-miRNA-mRNA network was successfully constructed based on the ceRNA hypothesis, including five lncRNAs (DLGAP1-AS1, GAS5, LINC00665, TYMSOS, and ZFAS1), six circRNAs (hsa_circ_0003209, hsa_circ_0008128, hsa_circ_0020396, hsa_circ_0030051, hsa_circ_0034049, and hsa_circ_0082333), eight miRNAs (hsa-miR-150-5p, hsa-miR-19b-3p, hsa-miR-23b-3p, hsa-miR-26a-5p, hsa-miR-651-5p, hsa-miR-10a-5p, hsa-miR-214-5p and hsa-miR-486-5p), and five mRNAs (CDC6, GINS1, MCM4, MCM6, and MCM7). The ceRNA network can promote HCC progression via cell cycle, DNA replication, and other pathways. Clinical diagnostic and survival analyses demonstrated that the ZFAS1/hsa-miR-150-5p/GINS1 ceRNA regulatory axis had a high diagnostic and prognostic value.Conclusion: These results revealed that cell cycle and DNA replication pathway could be potential pathways to participate in HCC development. The ceRNA network is expected to provide potential biomarkers and therapeutic targets for HCC management, especially the ZFAS1/hsa-miR-150-5p/GINS1 regulatory axis.

Background and Objective: Non-small cell lung cancer (NSCLC) accounts for 80–85% of all patients with lung cancer and 5-year relative overall survival (OS) rate is less than 20%, so that identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study attempted to identify potential key genes associated with the pathogenesis and prognosis of NSCLC.Methods: Four GEO datasets (GSE18842, GSE19804, GSE43458, and GSE62113) were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NSCLC samples and normal ones were analyzed using limma package, and RobustRankAggreg (RRA) package was used to conduct gene integration. Moreover, Search Tool for the Retrieval of Interacting Genes database (STRING), Cytoscape, and Molecular Complex Detection (MCODE) were utilized to establish protein–protein interaction (PPI) network of these DEGs. Furthermore, functional enrichment and pathway enrichment analyses for DEGs were performed by Funrich and OmicShare. While the expressions and prognostic values of top genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan Meier-plotter (KM) online dataset.Results: A total of 249 DEGs (113 upregulated and 136 downregulated) were identified after gene integration. Moreover, the PPI network was established with 166 nodes and 1784 protein pairs. Topoisomerase II alpha (TOP2A), a top gene and hub node with higher node degrees in module 1, was significantly enriched in mitotic cell cycle pathway. In addition, Interleukin-6 (IL-6) was enriched in amb2 integrin signaling pathway. The mitotic cell cycle was the most significant pathway in module 1 with the highest P-value. Besides, five hub genes with high degree of connectivity were selected, including TOP2A, CCNB1, CCNA2, UBE2C, and KIF20A, and they were all correlated with worse OS in NSCLC. Conclusion: The results showed that TOP2A, CCNB1, CCNA2, UBE2C, KIF20A, and IL-6 may be potential key genes, while the mitotic cell cycle pathway may be a potential pathway contribute to progression in NSCLC. Further, it could be used as a new biomarker for diagnosis and to direct the synthesis medicine of NSCLC.

BackgroundChronic obstructive pulmonary disease (COPD) is one of the most prevalent chronic respiratory diseases and the fourth cause of mortality globally. Neutrophilic inflammation has a vital role in the occurrence and progression of COPD. This study aimed to identify the novel hub genes involved in neutrophilic inflammation in COPD through bioinformatic prediction and experimental validation.MethodsBoth the single-cell RNA sequencing (scRNA-seq) dataset (GSE173896) and the RNA sequencing (RNA-seq) dataset (GSE57148) were downloaded from the Gene Expression Omnibus (GEO) database. The Seurat package was used for quality control, dimensions reduction, and cell identification of scRNA-seq. The irGSEA package was used for scoring individual cells. The Monocle2 package was used for the trajectory analysis of neutrophils. The CIBERSORT algorithm was used for analysis of immune cell infiltration in the lungs of COPD patients and controls in RNA-seq dataset, and weighted gene co-expression network analysis (WGCNA) correlated gene modules with neutrophil infiltration. The Mendelian randomization (MR) analysis explored the causal relationship between feature DEGs and COPD. The protein–protein interaction (PPI) network of novel hub genes was constructed, and real-time quantitative polymerase chain reaction (qRT-PCR) was used to validate novel hub genes in clinical specimens.ResultsIn scRNA-seq, the gene sets upregulated in COPD samples were related to the neutrophilic inflammatory response and TNF-α activation of the NF-κB signaling pathway. In RNA-seq, immune infiltration analysis showed neutrophils were upregulated in COPD lung tissue. We combined data from differential and modular genes and identified 51 differential genes associated with neutrophilic inflammation. Using MR analysis, 6 genes were explored to be causally associated with COPD. Meanwhile, 11 hub genes were identified by PPI network analysis, and all of them were upregulated. qRT-PCR experiments validated 9 out of 11 genes in peripheral blood leukocytes of COPD patients. Furthermore, 5 genes negatively correlated with lung function in COPD patients. Finally, a network of transcription factors for NAMPT and PTGS2 was constructed.ConclusionThis study identified nine novel hub genes related to the neutrophilic inflammation in COPD, and two genes were risk factors of COPD, which may serve as potential biomarkers for the clinical severity of COPD.